Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The constraints and data associated with SCUC are both geographically and temporally correlated to ensure the reliability of the solution, which further increases the complexity. In this paper, an advanced machine learning (ML) model is used to study the patterns in power system historical data, which inherently considers both spatial and temporal (ST) correlations in constraints. The ST-correlated ML model is trained to understand spatial correlation by considering graph neural networks (GNN) whereas temporal sequences are studied using long short-term memory (LSTM) networks. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina (SC) 500-Bus system. Moreover, B-{\theta} and power transfer distribution factor (PTDF) based SCUC formulations were considered in this research. Simulation results demonstrate that the ST approach can effectively predict generator commitment schedule and classify critical and non-critical lines in the system which are utilized for model reduction of SCUC to obtain computational enhancement without loss in solution quality

翻译：安全约束机组组合（SCUC）是电力系统日前调度与市场清出中计算复杂度极高的过程。该问题每日运行，需采用前沿算法加速求解。SCUC的约束条件与数据同时具有地理与时间相关性以确保解方案的可靠性，这进一步加剧了问题复杂度。本文采用先进机器学习模型研究电力系统历史数据模式，该模型天然兼顾约束条件中的时空相关性。基于图神经网络理解空间关联，利用长短期记忆网络研究时间序列，训练得到时空相关机器学习模型。通过IEEE 24节点系统、IEEE 73节点系统、IEEE 118节点系统及南卡罗来纳州500节点合成系统等多个测试系统验证了所提方法。此外，本研究考虑了基于B-θ和功率传输分布因子的SCUC公式。仿真结果表明，该时空方法可有效预测机组启停计划，并分类系统中的关键与非关键线路，据此实现SCUC模型约简，在保证解方案质量的前提下提升计算效率。